Real-Time Neural Hair Denoising

• URL: http://arxiv.org/abs/2605.17557v1
• Date: Sun, 17 May 2026 17:37:57 GMT
• Title: Real-Time Neural Hair Denoising
• Abstract summary: We propose a lightweight real-time method for reconstructing strand-based hair G- Buffers from severely undersampledized inputs.<n>Our pipeline first applies neural reconstruction and temporal accumulation to recover hair coverage, i.e., fractional hair visibility within a pixel, and tangent.<n>It then uses a tangent-guided reconstruction step to complete the position, which is subsequently used for physically based deferred hair shading.
• Score: 13.55744548465362
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a lightweight real-time method for reconstructing strand-based hair G-Buffers from severely undersampled rasterized inputs. Our pipeline first applies neural spatial reconstruction and temporal accumulation to recover hair coverage, i.e., fractional hair visibility within a pixel, and tangent. It then uses a tangent-guided reconstruction step to complete the position, which is subsequently used for physically based deferred hair shading. We evaluate our method across a diverse set of hairstyles, including straight, wavy, afro, and ponytail styles, under both static and dynamic scenarios. Our method achieves higher hair reconstruction quality than existing hair-specific denoising techniques and general industrial neural reconstruction solutions such as DLSS and FSR.

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